Overview

Brought to you by YData

Dataset statistics

Number of variables52
Number of observations30522
Missing cells0
Missing cells (%)0.0%
Total size in memory13.3 MiB
Average record size in memory458.6 B

Variable types

Text39
Numeric12
DateTime1

Alerts

acetohexamide has constant value "No" Constant
examide has constant value "No" Constant
citoglipton has constant value "No" Constant
metformin_rosiglitazone has constant value "No" Constant
split has constant value "train" Constant
load_date has constant value "2025-04-29" Constant
number_emergency is highly skewed (γ1 = 23.20772443) Skewed
encounter_id has unique values Unique
num_procedures has 14017 (45.9%) zeros Zeros
number_outpatient has 25403 (83.2%) zeros Zeros
number_emergency has 27045 (88.6%) zeros Zeros
number_inpatient has 20190 (66.1%) zeros Zeros

Reproduction

Analysis started2025-04-29 05:12:25.530896
Analysis finished2025-04-29 05:12:32.169491
Duration6.64 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

encounter_id
Text

Unique 

Distinct30522
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:12:35.004592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.680689339
Min length5

Characters and Unicode

Total characters264952
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30522 ?
Unique (%)100.0%

Sample

1st row57540318
2nd row165160488
3rd row158354610
4th row369705920
5th row53094960
ValueCountFrequency (%)
57540318 1
 
< 0.1%
150609006 1
 
< 0.1%
155689548 1
 
< 0.1%
62599788 1
 
< 0.1%
277007994 1
 
< 0.1%
310163198 1
 
< 0.1%
125952792 1
 
< 0.1%
176175204 1
 
< 0.1%
42078624 1
 
< 0.1%
263608194 1
 
< 0.1%
Other values (30512) 30512
> 99.9%
2025-04-29T05:12:42.201166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 33548
12.7%
1 32428
12.2%
4 28780
10.9%
6 28079
10.6%
8 27394
10.3%
0 26937
10.2%
3 23593
8.9%
5 22047
8.3%
7 21254
8.0%
9 20892
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 264952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 33548
12.7%
1 32428
12.2%
4 28780
10.9%
6 28079
10.6%
8 27394
10.3%
0 26937
10.2%
3 23593
8.9%
5 22047
8.3%
7 21254
8.0%
9 20892
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 264952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 33548
12.7%
1 32428
12.2%
4 28780
10.9%
6 28079
10.6%
8 27394
10.3%
0 26937
10.2%
3 23593
8.9%
5 22047
8.3%
7 21254
8.0%
9 20892
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 264952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 33548
12.7%
1 32428
12.2%
4 28780
10.9%
6 28079
10.6%
8 27394
10.3%
0 26937
10.2%
3 23593
8.9%
5 22047
8.3%
7 21254
8.0%
9 20892
7.9%

patient_nbr
Real number (ℝ)

Distinct26394
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54365087.13
Minimum135
Maximum189502619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:12:44.744644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1411526.7
Q123420119.5
median45648909
Q387567284.25
95-th percentile111287717.5
Maximum189502619
Range189502484
Interquartile range (IQR)64147164.75

Descriptive statistics

Standard deviation38634012.22
Coefficient of variation (CV)0.7106401233
Kurtosis-0.3521688676
Mean54365087.13
Median Absolute Deviation (MAD)32892250.5
Skewness0.4634333449
Sum1.659331189 × 1012
Variance1.4925869 × 1015
MonotonicityNot monotonic
2025-04-29T05:12:48.997105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88785891 16
 
0.1%
1660293 10
 
< 0.1%
89472402 10
 
< 0.1%
23199021 9
 
< 0.1%
41699412 9
 
< 0.1%
84676248 9
 
< 0.1%
43140906 8
 
< 0.1%
37096866 8
 
< 0.1%
41371992 7
 
< 0.1%
84428613 7
 
< 0.1%
Other values (26384) 30429
99.7%
ValueCountFrequency (%)
135 2
< 0.1%
378 1
 
< 0.1%
774 1
 
< 0.1%
927 1
 
< 0.1%
1152 3
< 0.1%
ValueCountFrequency (%)
189502619 1
< 0.1%
189365864 1
< 0.1%
189332087 1
< 0.1%
189169511 1
< 0.1%
189075614 1
< 0.1%

race
Text

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:12:52.482159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length9
Mean length9.865211978
Min length1

Characters and Unicode

Total characters301106
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfricanAmerican
2nd rowCaucasian
3rd rowCaucasian
4th rowCaucasian
5th rowCaucasian
ValueCountFrequency (%)
caucasian 22794
74.7%
africanamerican 5816
 
19.1%
663
 
2.2%
hispanic 604
 
2.0%
other 449
 
1.5%
asian 196
 
0.6%
2025-04-29T05:12:57.000570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 80814
26.8%
i 35830
11.9%
n 35226
11.7%
c 35030
11.6%
s 23594
 
7.8%
C 22794
 
7.6%
u 22794
 
7.6%
r 12081
 
4.0%
A 11828
 
3.9%
e 6265
 
2.1%
Other values (8) 14850
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 301106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 80814
26.8%
i 35830
11.9%
n 35226
11.7%
c 35030
11.6%
s 23594
 
7.8%
C 22794
 
7.6%
u 22794
 
7.6%
r 12081
 
4.0%
A 11828
 
3.9%
e 6265
 
2.1%
Other values (8) 14850
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 301106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 80814
26.8%
i 35830
11.9%
n 35226
11.7%
c 35030
11.6%
s 23594
 
7.8%
C 22794
 
7.6%
u 22794
 
7.6%
r 12081
 
4.0%
A 11828
 
3.9%
e 6265
 
2.1%
Other values (8) 14850
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 301106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 80814
26.8%
i 35830
11.9%
n 35226
11.7%
c 35030
11.6%
s 23594
 
7.8%
C 22794
 
7.6%
u 22794
 
7.6%
r 12081
 
4.0%
A 11828
 
3.9%
e 6265
 
2.1%
Other values (8) 14850
 
4.9%

gender
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:12:58.096534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length6
Mean length5.080957998
Min length4

Characters and Unicode

Total characters155081
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale
ValueCountFrequency (%)
female 16491
54.0%
male 14030
46.0%
unknown/invalid 1
 
< 0.1%
2025-04-29T05:13:02.686730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 47012
30.3%
a 30522
19.7%
l 30522
19.7%
F 16491
 
10.6%
m 16491
 
10.6%
M 14030
 
9.0%
n 4
 
< 0.1%
U 1
 
< 0.1%
k 1
 
< 0.1%
o 1
 
< 0.1%
Other values (6) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 155081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 47012
30.3%
a 30522
19.7%
l 30522
19.7%
F 16491
 
10.6%
m 16491
 
10.6%
M 14030
 
9.0%
n 4
 
< 0.1%
U 1
 
< 0.1%
k 1
 
< 0.1%
o 1
 
< 0.1%
Other values (6) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 155081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 47012
30.3%
a 30522
19.7%
l 30522
19.7%
F 16491
 
10.6%
m 16491
 
10.6%
M 14030
 
9.0%
n 4
 
< 0.1%
U 1
 
< 0.1%
k 1
 
< 0.1%
o 1
 
< 0.1%
Other values (6) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 155081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 47012
30.3%
a 30522
19.7%
l 30522
19.7%
F 16491
 
10.6%
m 16491
 
10.6%
M 14030
 
9.0%
n 4
 
< 0.1%
U 1
 
< 0.1%
k 1
 
< 0.1%
o 1
 
< 0.1%
Other values (6) 6
 
< 0.1%

age
Text

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:02.855100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.025227705
Min length6

Characters and Unicode

Total characters214424
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[50-60)
2nd row[80-90)
3rd row[50-60)
4th row[70-80)
5th row[60-70)
ValueCountFrequency (%)
70-80 7812
25.6%
60-70 6792
22.3%
50-60 5232
17.1%
80-90 5117
16.8%
40-50 2831
 
9.3%
30-40 1137
 
3.7%
90-100 829
 
2.7%
20-30 481
 
1.6%
10-20 232
 
0.8%
0-10 59
 
0.2%
2025-04-29T05:13:03.254678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 61873
28.9%
[ 30522
14.2%
- 30522
14.2%
) 30522
14.2%
7 14604
 
6.8%
8 12929
 
6.0%
6 12024
 
5.6%
5 8063
 
3.8%
9 5946
 
2.8%
4 3968
 
1.9%
Other values (3) 3451
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 214424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61873
28.9%
[ 30522
14.2%
- 30522
14.2%
) 30522
14.2%
7 14604
 
6.8%
8 12929
 
6.0%
6 12024
 
5.6%
5 8063
 
3.8%
9 5946
 
2.8%
4 3968
 
1.9%
Other values (3) 3451
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 214424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61873
28.9%
[ 30522
14.2%
- 30522
14.2%
) 30522
14.2%
7 14604
 
6.8%
8 12929
 
6.0%
6 12024
 
5.6%
5 8063
 
3.8%
9 5946
 
2.8%
4 3968
 
1.9%
Other values (3) 3451
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 214424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61873
28.9%
[ 30522
14.2%
- 30522
14.2%
) 30522
14.2%
7 14604
 
6.8%
8 12929
 
6.0%
6 12024
 
5.6%
5 8063
 
3.8%
9 5946
 
2.8%
4 3968
 
1.9%
Other values (3) 3451
 
1.6%

weight
Text

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:03.405483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length1
Mean length1.204278881
Min length1

Characters and Unicode

Total characters36757
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?
ValueCountFrequency (%)
29618
97.0%
75-100 375
 
1.2%
50-75 258
 
0.8%
100-125 177
 
0.6%
125-150 37
 
0.1%
25-50 27
 
0.1%
0-25 14
 
< 0.1%
150-175 10
 
< 0.1%
175-200 4
 
< 0.1%
200 2
 
< 0.1%
2025-04-29T05:13:03.850286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 29618
80.6%
0 1462
 
4.0%
5 1234
 
3.4%
[ 902
 
2.5%
- 902
 
2.5%
) 902
 
2.5%
1 827
 
2.2%
7 647
 
1.8%
2 261
 
0.7%
> 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 29618
80.6%
0 1462
 
4.0%
5 1234
 
3.4%
[ 902
 
2.5%
- 902
 
2.5%
) 902
 
2.5%
1 827
 
2.2%
7 647
 
1.8%
2 261
 
0.7%
> 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 29618
80.6%
0 1462
 
4.0%
5 1234
 
3.4%
[ 902
 
2.5%
- 902
 
2.5%
) 902
 
2.5%
1 827
 
2.2%
7 647
 
1.8%
2 261
 
0.7%
> 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 29618
80.6%
0 1462
 
4.0%
5 1234
 
3.4%
[ 902
 
2.5%
- 902
 
2.5%
) 902
 
2.5%
1 827
 
2.2%
7 647
 
1.8%
2 261
 
0.7%
> 2
 
< 0.1%

admission_type_id
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.032959832
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:04.039083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.45413416
Coefficient of variation (CV)0.7152793368
Kurtosis1.837936029
Mean2.032959832
Median Absolute Deviation (MAD)0
Skewness1.573667816
Sum62050
Variance2.114506157
MonotonicityNot monotonic
2025-04-29T05:13:04.210016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 16152
52.9%
3 5650
 
18.5%
2 5525
 
18.1%
6 1641
 
5.4%
5 1454
 
4.8%
8 88
 
0.3%
7 10
 
< 0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
1 16152
52.9%
2 5525
 
18.1%
3 5650
 
18.5%
4 2
 
< 0.1%
5 1454
 
4.8%
ValueCountFrequency (%)
8 88
 
0.3%
7 10
 
< 0.1%
6 1641
5.4%
5 1454
4.8%
4 2
 
< 0.1%

discharge_disposition_id
Real number (ℝ)

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.742218727
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:04.432745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.290665962
Coefficient of variation (CV)1.413777854
Kurtosis5.925035396
Mean3.742218727
Median Absolute Deviation (MAD)0
Skewness2.546854376
Sum114220
Variance27.99114632
MonotonicityNot monotonic
2025-04-29T05:13:04.661648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 17945
58.8%
3 4156
 
13.6%
6 3973
 
13.0%
18 1107
 
3.6%
2 658
 
2.2%
22 592
 
1.9%
11 516
 
1.7%
5 357
 
1.2%
25 307
 
1.0%
4 253
 
0.8%
Other values (15) 658
 
2.2%
ValueCountFrequency (%)
1 17945
58.8%
2 658
 
2.2%
3 4156
 
13.6%
4 253
 
0.8%
5 357
 
1.2%
ValueCountFrequency (%)
28 38
 
0.1%
27 1
 
< 0.1%
25 307
1.0%
24 24
 
0.1%
23 118
 
0.4%

admission_source_id
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.749754276
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:04.931785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum20
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.061664779
Coefficient of variation (CV)0.7064066713
Kurtosis1.727232509
Mean5.749754276
Median Absolute Deviation (MAD)0
Skewness1.027765834
Sum175494
Variance16.49712077
MonotonicityNot monotonic
2025-04-29T05:13:05.161897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7 17242
56.5%
1 8874
29.1%
17 2040
 
6.7%
4 969
 
3.2%
6 625
 
2.0%
2 320
 
1.0%
5 299
 
1.0%
3 68
 
0.2%
20 47
 
0.2%
9 29
 
0.1%
Other values (3) 9
 
< 0.1%
ValueCountFrequency (%)
1 8874
29.1%
2 320
 
1.0%
3 68
 
0.2%
4 969
 
3.2%
5 299
 
1.0%
ValueCountFrequency (%)
20 47
 
0.2%
17 2040
6.7%
14 1
 
< 0.1%
10 1
 
< 0.1%
9 29
 
0.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.39142258
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:05.345109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.986874701
Coefficient of variation (CV)0.6801610745
Kurtosis0.8639793739
Mean4.39142258
Median Absolute Deviation (MAD)2
Skewness1.139536812
Sum134035
Variance8.921420479
MonotonicityNot monotonic
2025-04-29T05:13:05.545782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 5356
17.5%
2 5160
16.9%
1 4284
14.0%
4 4115
13.5%
5 3001
9.8%
6 2337
7.7%
7 1730
 
5.7%
8 1283
 
4.2%
9 849
 
2.8%
10 724
 
2.4%
Other values (4) 1683
 
5.5%
ValueCountFrequency (%)
1 4284
14.0%
2 5160
16.9%
3 5356
17.5%
4 4115
13.5%
5 3001
9.8%
ValueCountFrequency (%)
14 307
1.0%
13 375
1.2%
12 437
1.4%
11 564
1.8%
10 724
2.4%
Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:05.685569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.602254112
Min length1

Characters and Unicode

Total characters48904
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd rowMC
3rd row?
4th rowMC
5th row?
ValueCountFrequency (%)
12140
39.8%
mc 9628
31.5%
hm 1922
 
6.3%
sp 1512
 
5.0%
bc 1383
 
4.5%
md 1087
 
3.6%
cp 747
 
2.4%
un 727
 
2.4%
cm 570
 
1.9%
og 314
 
1.0%
Other values (7) 492
 
1.6%
2025-04-29T05:13:06.120124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 13399
27.4%
C 12412
25.4%
? 12140
24.8%
P 2452
 
5.0%
H 1972
 
4.0%
S 1527
 
3.1%
B 1383
 
2.8%
D 1264
 
2.6%
U 727
 
1.5%
N 727
 
1.5%
Other values (5) 901
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 13399
27.4%
C 12412
25.4%
? 12140
24.8%
P 2452
 
5.0%
H 1972
 
4.0%
S 1527
 
3.1%
B 1383
 
2.8%
D 1264
 
2.6%
U 727
 
1.5%
N 727
 
1.5%
Other values (5) 901
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 13399
27.4%
C 12412
25.4%
? 12140
24.8%
P 2452
 
5.0%
H 1972
 
4.0%
S 1527
 
3.1%
B 1383
 
2.8%
D 1264
 
2.6%
U 727
 
1.5%
N 727
 
1.5%
Other values (5) 901
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 13399
27.4%
C 12412
25.4%
? 12140
24.8%
P 2452
 
5.0%
H 1972
 
4.0%
S 1527
 
3.1%
B 1383
 
2.8%
D 1264
 
2.6%
U 727
 
1.5%
N 727
 
1.5%
Other values (5) 901
 
1.8%
Distinct65
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:06.329198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length33
Mean length8.581547736
Min length1

Characters and Unicode

Total characters261926
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row?
2nd rowInternalMedicine
3rd row?
4th rowEmergency/Trauma
5th rowCardiology
ValueCountFrequency (%)
15064
49.4%
internalmedicine 4295
 
14.1%
family/generalpractice 2295
 
7.5%
emergency/trauma 2233
 
7.3%
cardiology 1659
 
5.4%
surgery-general 891
 
2.9%
nephrology 511
 
1.7%
orthopedics 432
 
1.4%
orthopedics-reconstructive 367
 
1.2%
radiologist 344
 
1.1%
Other values (55) 2431
 
8.0%
2025-04-29T05:13:06.825373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 31254
 
11.9%
r 22873
 
8.7%
a 21365
 
8.2%
n 20328
 
7.8%
i 19053
 
7.3%
? 15064
 
5.8%
c 14995
 
5.7%
l 14696
 
5.6%
y 10444
 
4.0%
o 10254
 
3.9%
Other values (32) 81600
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261926
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 31254
 
11.9%
r 22873
 
8.7%
a 21365
 
8.2%
n 20328
 
7.8%
i 19053
 
7.3%
? 15064
 
5.8%
c 14995
 
5.7%
l 14696
 
5.6%
y 10444
 
4.0%
o 10254
 
3.9%
Other values (32) 81600
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261926
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 31254
 
11.9%
r 22873
 
8.7%
a 21365
 
8.2%
n 20328
 
7.8%
i 19053
 
7.3%
? 15064
 
5.8%
c 14995
 
5.7%
l 14696
 
5.6%
y 10444
 
4.0%
o 10254
 
3.9%
Other values (32) 81600
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261926
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 31254
 
11.9%
r 22873
 
8.7%
a 21365
 
8.2%
n 20328
 
7.8%
i 19053
 
7.3%
? 15064
 
5.8%
c 14995
 
5.7%
l 14696
 
5.6%
y 10444
 
4.0%
o 10254
 
3.9%
Other values (32) 81600
31.2%

num_lab_procedures
Real number (ℝ)

Distinct109
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.08410327
Minimum1
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:07.135763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum129
Range128
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.76096558
Coefficient of variation (CV)0.45866025
Kurtosis-0.2549683766
Mean43.08410327
Median Absolute Deviation (MAD)13
Skewness-0.2352965276
Sum1315013
Variance390.4957605
MonotonicityNot monotonic
2025-04-29T05:13:07.483166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 980
 
3.2%
43 838
 
2.7%
44 750
 
2.5%
46 695
 
2.3%
45 688
 
2.3%
38 668
 
2.2%
40 649
 
2.1%
41 648
 
2.1%
51 624
 
2.0%
42 624
 
2.0%
Other values (99) 23358
76.5%
ValueCountFrequency (%)
1 980
3.2%
2 323
 
1.1%
3 200
 
0.7%
4 120
 
0.4%
5 93
 
0.3%
ValueCountFrequency (%)
129 1
 
< 0.1%
113 2
< 0.1%
109 1
 
< 0.1%
108 3
< 0.1%
106 3
< 0.1%

num_procedures
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.338903086
Minimum0
Maximum6
Zeros14017
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:07.678200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.70437608
Coefficient of variation (CV)1.272964487
Kurtosis0.8464549386
Mean1.338903086
Median Absolute Deviation (MAD)1
Skewness1.311986299
Sum40866
Variance2.904897824
MonotonicityNot monotonic
2025-04-29T05:13:07.864320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 14017
45.9%
1 6199
20.3%
2 3759
 
12.3%
3 2896
 
9.5%
6 1474
 
4.8%
4 1268
 
4.2%
5 909
 
3.0%
ValueCountFrequency (%)
0 14017
45.9%
1 6199
20.3%
2 3759
 
12.3%
3 2896
 
9.5%
4 1268
 
4.2%
ValueCountFrequency (%)
6 1474
 
4.8%
5 909
 
3.0%
4 1268
 
4.2%
3 2896
9.5%
2 3759
12.3%

num_medications
Real number (ℝ)

Distinct71
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.04445973
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:08.069963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median15
Q320
95-th percentile31
Maximum75
Range74
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.054218707
Coefficient of variation (CV)0.5019937624
Kurtosis3.332246187
Mean16.04445973
Median Absolute Deviation (MAD)5
Skewness1.28287026
Sum489709
Variance64.87043898
MonotonicityNot monotonic
2025-04-29T05:13:08.389825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 1790
 
5.9%
13 1772
 
5.8%
12 1769
 
5.8%
15 1737
 
5.7%
11 1713
 
5.6%
16 1630
 
5.3%
10 1604
 
5.3%
17 1466
 
4.8%
18 1421
 
4.7%
9 1414
 
4.6%
Other values (61) 14206
46.5%
ValueCountFrequency (%)
1 74
 
0.2%
2 128
 
0.4%
3 281
0.9%
4 439
1.4%
5 613
2.0%
ValueCountFrequency (%)
75 1
< 0.1%
72 1
< 0.1%
70 1
< 0.1%
69 2
< 0.1%
68 2
< 0.1%

number_outpatient
Real number (ℝ)

Zeros 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3754668764
Minimum0
Maximum42
Zeros25403
Zeros (%)83.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:08.652267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.270900425
Coefficient of variation (CV)3.384853645
Kurtosis161.8831812
Mean0.3754668764
Median Absolute Deviation (MAD)0
Skewness9.032133915
Sum11460
Variance1.61518789
MonotonicityNot monotonic
2025-04-29T05:13:08.855331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 25403
83.2%
1 2615
 
8.6%
2 1087
 
3.6%
3 626
 
2.1%
4 338
 
1.1%
5 185
 
0.6%
6 89
 
0.3%
7 47
 
0.2%
8 24
 
0.1%
9 23
 
0.1%
Other values (20) 85
 
0.3%
ValueCountFrequency (%)
0 25403
83.2%
1 2615
 
8.6%
2 1087
 
3.6%
3 626
 
2.1%
4 338
 
1.1%
ValueCountFrequency (%)
42 1
< 0.1%
40 1
< 0.1%
36 1
< 0.1%
33 1
< 0.1%
27 1
< 0.1%

number_emergency
Real number (ℝ)

Skewed  Zeros 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2057859904
Minimum0
Maximum64
Zeros27045
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:09.052174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum64
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9961173962
Coefficient of variation (CV)4.840550098
Kurtosis1066.725521
Mean0.2057859904
Median Absolute Deviation (MAD)0
Skewness23.20772443
Sum6281
Variance0.9922498671
MonotonicityNot monotonic
2025-04-29T05:13:09.278591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 27045
88.6%
1 2305
 
7.6%
2 657
 
2.2%
3 226
 
0.7%
4 110
 
0.4%
5 60
 
0.2%
6 30
 
0.1%
7 22
 
0.1%
8 14
 
< 0.1%
9 12
 
< 0.1%
Other values (15) 41
 
0.1%
ValueCountFrequency (%)
0 27045
88.6%
1 2305
 
7.6%
2 657
 
2.2%
3 226
 
0.7%
4 110
 
0.4%
ValueCountFrequency (%)
64 1
< 0.1%
54 1
< 0.1%
42 1
< 0.1%
37 1
< 0.1%
29 1
< 0.1%

number_inpatient
Real number (ℝ)

Zeros 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.644289365
Minimum0
Maximum18
Zeros20190
Zeros (%)66.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:09.535558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.275325662
Coefficient of variation (CV)1.979429944
Kurtosis20.77885959
Mean0.644289365
Median Absolute Deviation (MAD)0
Skewness3.626326826
Sum19665
Variance1.626455543
MonotonicityNot monotonic
2025-04-29T05:13:09.781413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 20190
66.1%
1 5873
 
19.2%
2 2292
 
7.5%
3 1077
 
3.5%
4 481
 
1.6%
5 247
 
0.8%
6 135
 
0.4%
7 84
 
0.3%
8 41
 
0.1%
9 36
 
0.1%
Other values (9) 66
 
0.2%
ValueCountFrequency (%)
0 20190
66.1%
1 5873
 
19.2%
2 2292
 
7.5%
3 1077
 
3.5%
4 481
 
1.6%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 1
 
< 0.1%
16 3
< 0.1%
15 2
< 0.1%
14 4
< 0.1%

diag_1
Text

Distinct605
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:10.290502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.17888736
Min length1

Characters and Unicode

Total characters97026
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)0.4%

Sample

1st row486
2nd row466
3rd row572
4th row599
5th row578
ValueCountFrequency (%)
414 2066
 
6.8%
428 2064
 
6.8%
786 1222
 
4.0%
486 1102
 
3.6%
410 1091
 
3.6%
427 834
 
2.7%
491 692
 
2.3%
715 657
 
2.2%
434 642
 
2.1%
996 634
 
2.1%
Other values (595) 19518
63.9%
2025-04-29T05:13:11.168010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 16920
17.4%
2 11936
12.3%
8 11403
11.8%
5 11048
11.4%
1 8523
8.8%
7 8474
8.7%
0 7360
7.6%
6 7045
7.3%
9 5959
 
6.1%
3 5251
 
5.4%
Other values (3) 3107
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 16920
17.4%
2 11936
12.3%
8 11403
11.8%
5 11048
11.4%
1 8523
8.8%
7 8474
8.7%
0 7360
7.6%
6 7045
7.3%
9 5959
 
6.1%
3 5251
 
5.4%
Other values (3) 3107
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 16920
17.4%
2 11936
12.3%
8 11403
11.8%
5 11048
11.4%
1 8523
8.8%
7 8474
8.7%
0 7360
7.6%
6 7045
7.3%
9 5959
 
6.1%
3 5251
 
5.4%
Other values (3) 3107
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 16920
17.4%
2 11936
12.3%
8 11403
11.8%
5 11048
11.4%
1 8523
8.8%
7 8474
8.7%
0 7360
7.6%
6 7045
7.3%
9 5959
 
6.1%
3 5251
 
5.4%
Other values (3) 3107
 
3.2%

diag_2
Text

Distinct592
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:11.656348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.16519232
Min length1

Characters and Unicode

Total characters96608
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)0.4%

Sample

1st rowV42
2nd row473
3rd row571
4th row250
5th row557
ValueCountFrequency (%)
428 2072
 
6.8%
276 1990
 
6.5%
250 1792
 
5.9%
427 1507
 
4.9%
401 1128
 
3.7%
599 1033
 
3.4%
496 986
 
3.2%
403 858
 
2.8%
414 788
 
2.6%
411 784
 
2.6%
Other values (582) 17584
57.6%
2025-04-29T05:13:12.444924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 15518
16.1%
2 14918
15.4%
5 11309
11.7%
0 10249
10.6%
8 8492
8.8%
7 8452
8.7%
1 7876
8.2%
9 6643
6.9%
6 5997
 
6.2%
3 4292
 
4.4%
Other values (4) 2862
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 15518
16.1%
2 14918
15.4%
5 11309
11.7%
0 10249
10.6%
8 8492
8.8%
7 8452
8.7%
1 7876
8.2%
9 6643
6.9%
6 5997
 
6.2%
3 4292
 
4.4%
Other values (4) 2862
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 15518
16.1%
2 14918
15.4%
5 11309
11.7%
0 10249
10.6%
8 8492
8.8%
7 8452
8.7%
1 7876
8.2%
9 6643
6.9%
6 5997
 
6.2%
3 4292
 
4.4%
Other values (4) 2862
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 15518
16.1%
2 14918
15.4%
5 11309
11.7%
0 10249
10.6%
8 8492
8.8%
7 8452
8.7%
1 7876
8.2%
9 6643
6.9%
6 5997
 
6.2%
3 4292
 
4.4%
Other values (4) 2862
 
3.0%

diag_3
Text

Distinct622
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:12.945710image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.112345194
Min length1

Characters and Unicode

Total characters94995
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)0.4%

Sample

1st row250.92
2nd row780
3rd row287
4th row401
5th row427
ValueCountFrequency (%)
250 3429
 
11.2%
401 2478
 
8.1%
276 1532
 
5.0%
428 1395
 
4.6%
427 1153
 
3.8%
414 1082
 
3.5%
496 797
 
2.6%
403 720
 
2.4%
585 628
 
2.1%
272 585
 
1.9%
Other values (612) 16723
54.8%
2025-04-29T05:13:13.631569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 15411
16.2%
4 14827
15.6%
5 12407
13.1%
0 11809
12.4%
7 7931
8.3%
1 7328
7.7%
8 7168
7.5%
9 5244
 
5.5%
6 4966
 
5.2%
3 4283
 
4.5%
Other values (4) 3621
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15411
16.2%
4 14827
15.6%
5 12407
13.1%
0 11809
12.4%
7 7931
8.3%
1 7328
7.7%
8 7168
7.5%
9 5244
 
5.5%
6 4966
 
5.2%
3 4283
 
4.5%
Other values (4) 3621
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15411
16.2%
4 14827
15.6%
5 12407
13.1%
0 11809
12.4%
7 7931
8.3%
1 7328
7.7%
8 7168
7.5%
9 5244
 
5.5%
6 4966
 
5.2%
3 4283
 
4.5%
Other values (4) 3621
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15411
16.2%
4 14827
15.6%
5 12407
13.1%
0 11809
12.4%
7 7931
8.3%
1 7328
7.7%
8 7168
7.5%
9 5244
 
5.5%
6 4966
 
5.2%
3 4283
 
4.5%
Other values (4) 3621
 
3.8%

number_diagnoses
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.42425136
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:13.822253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.926786219
Coefficient of variation (CV)0.2595259946
Kurtosis-0.08372484263
Mean7.42425136
Median Absolute Deviation (MAD)1
Skewness-0.9012866173
Sum226603
Variance3.712505132
MonotonicityNot monotonic
2025-04-29T05:13:14.092383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
9 14834
48.6%
5 3424
 
11.2%
7 3194
 
10.5%
8 3173
 
10.4%
6 3042
 
10.0%
4 1605
 
5.3%
3 830
 
2.7%
2 328
 
1.1%
1 66
 
0.2%
16 11
 
< 0.1%
Other values (5) 15
 
< 0.1%
ValueCountFrequency (%)
1 66
 
0.2%
2 328
 
1.1%
3 830
 
2.7%
4 1605
5.3%
5 3424
11.2%
ValueCountFrequency (%)
16 11
< 0.1%
15 1
 
< 0.1%
13 5
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:14.250393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.052519494
Min length3

Characters and Unicode

Total characters93169
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNaN
2nd rowNaN
3rd rowNaN
4th rowNaN
5th rowNaN
ValueCountFrequency (%)
nan 28919
94.7%
norm 781
 
2.6%
200 445
 
1.5%
300 377
 
1.2%
2025-04-29T05:13:14.728692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 58619
62.9%
a 28919
31.0%
0 1644
 
1.8%
> 822
 
0.9%
o 781
 
0.8%
r 781
 
0.8%
m 781
 
0.8%
2 445
 
0.5%
3 377
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93169
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 58619
62.9%
a 28919
31.0%
0 1644
 
1.8%
> 822
 
0.9%
o 781
 
0.8%
r 781
 
0.8%
m 781
 
0.8%
2 445
 
0.5%
3 377
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93169
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 58619
62.9%
a 28919
31.0%
0 1644
 
1.8%
> 822
 
0.9%
o 781
 
0.8%
r 781
 
0.8%
m 781
 
0.8%
2 445
 
0.5%
3 377
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93169
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 58619
62.9%
a 28919
31.0%
0 1644
 
1.8%
> 822
 
0.9%
o 781
 
0.8%
r 781
 
0.8%
m 781
 
0.8%
2 445
 
0.5%
3 377
 
0.4%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:14.859066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.930836773
Min length2

Characters and Unicode

Total characters89455
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNaN
2nd rowNaN
3rd rowNaN
4th rowNaN
5th rowNaN
ValueCountFrequency (%)
nan 25347
83.0%
8 2475
 
8.1%
norm 1532
 
5.0%
7 1168
 
3.8%
2025-04-29T05:13:15.305726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 52226
58.4%
a 25347
28.3%
> 3643
 
4.1%
8 2475
 
2.8%
o 1532
 
1.7%
r 1532
 
1.7%
m 1532
 
1.7%
7 1168
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 52226
58.4%
a 25347
28.3%
> 3643
 
4.1%
8 2475
 
2.8%
o 1532
 
1.7%
r 1532
 
1.7%
m 1532
 
1.7%
7 1168
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 52226
58.4%
a 25347
28.3%
> 3643
 
4.1%
8 2475
 
2.8%
o 1532
 
1.7%
r 1532
 
1.7%
m 1532
 
1.7%
7 1168
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 52226
58.4%
a 25347
28.3%
> 3643
 
4.1%
8 2475
 
2.8%
o 1532
 
1.7%
r 1532
 
1.7%
m 1532
 
1.7%
7 1168
 
1.3%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:15.440425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.732455278
Min length2

Characters and Unicode

Total characters83400
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowSteady
3rd rowNo
4th rowNo
5th rowSteady
ValueCountFrequency (%)
no 24531
80.4%
steady 5505
 
18.0%
up 318
 
1.0%
down 168
 
0.6%
2025-04-29T05:13:15.782959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 24699
29.6%
N 24531
29.4%
S 5505
 
6.6%
t 5505
 
6.6%
e 5505
 
6.6%
a 5505
 
6.6%
d 5505
 
6.6%
y 5505
 
6.6%
U 318
 
0.4%
p 318
 
0.4%
Other values (3) 504
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 24699
29.6%
N 24531
29.4%
S 5505
 
6.6%
t 5505
 
6.6%
e 5505
 
6.6%
a 5505
 
6.6%
d 5505
 
6.6%
y 5505
 
6.6%
U 318
 
0.4%
p 318
 
0.4%
Other values (3) 504
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 24699
29.6%
N 24531
29.4%
S 5505
 
6.6%
t 5505
 
6.6%
e 5505
 
6.6%
a 5505
 
6.6%
d 5505
 
6.6%
y 5505
 
6.6%
U 318
 
0.4%
p 318
 
0.4%
Other values (3) 504
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 24699
29.6%
N 24531
29.4%
S 5505
 
6.6%
t 5505
 
6.6%
e 5505
 
6.6%
a 5505
 
6.6%
d 5505
 
6.6%
y 5505
 
6.6%
U 318
 
0.4%
p 318
 
0.4%
Other values (3) 504
 
0.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:15.898265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.053797261
Min length2

Characters and Unicode

Total characters62686
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30069
98.5%
steady 403
 
1.3%
up 35
 
0.1%
down 15
 
< 0.1%
2025-04-29T05:13:16.276812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 30084
48.0%
N 30069
48.0%
S 403
 
0.6%
t 403
 
0.6%
e 403
 
0.6%
a 403
 
0.6%
d 403
 
0.6%
y 403
 
0.6%
U 35
 
0.1%
p 35
 
0.1%
Other values (3) 45
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 30084
48.0%
N 30069
48.0%
S 403
 
0.6%
t 403
 
0.6%
e 403
 
0.6%
a 403
 
0.6%
d 403
 
0.6%
y 403
 
0.6%
U 35
 
0.1%
p 35
 
0.1%
Other values (3) 45
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 30084
48.0%
N 30069
48.0%
S 403
 
0.6%
t 403
 
0.6%
e 403
 
0.6%
a 403
 
0.6%
d 403
 
0.6%
y 403
 
0.6%
U 35
 
0.1%
p 35
 
0.1%
Other values (3) 45
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 30084
48.0%
N 30069
48.0%
S 403
 
0.6%
t 403
 
0.6%
e 403
 
0.6%
a 403
 
0.6%
d 403
 
0.6%
y 403
 
0.6%
U 35
 
0.1%
p 35
 
0.1%
Other values (3) 45
 
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:16.459242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.025489811
Min length2

Characters and Unicode

Total characters61822
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30317
99.3%
steady 193
 
0.6%
up 9
 
< 0.1%
down 3
 
< 0.1%
2025-04-29T05:13:16.825125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 30320
49.0%
N 30317
49.0%
S 193
 
0.3%
t 193
 
0.3%
e 193
 
0.3%
a 193
 
0.3%
d 193
 
0.3%
y 193
 
0.3%
U 9
 
< 0.1%
p 9
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 30320
49.0%
N 30317
49.0%
S 193
 
0.3%
t 193
 
0.3%
e 193
 
0.3%
a 193
 
0.3%
d 193
 
0.3%
y 193
 
0.3%
U 9
 
< 0.1%
p 9
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 30320
49.0%
N 30317
49.0%
S 193
 
0.3%
t 193
 
0.3%
e 193
 
0.3%
a 193
 
0.3%
d 193
 
0.3%
y 193
 
0.3%
U 9
 
< 0.1%
p 9
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 30320
49.0%
N 30317
49.0%
S 193
 
0.3%
t 193
 
0.3%
e 193
 
0.3%
a 193
 
0.3%
d 193
 
0.3%
y 193
 
0.3%
U 9
 
< 0.1%
p 9
 
< 0.1%
Other values (3) 9
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:16.942341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.003014219
Min length2

Characters and Unicode

Total characters61136
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30497
99.9%
steady 23
 
0.1%
up 2
 
< 0.1%
2025-04-29T05:13:17.466646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30497
49.9%
o 30497
49.9%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
U 2
 
< 0.1%
p 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30497
49.9%
o 30497
49.9%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
U 2
 
< 0.1%
p 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30497
49.9%
o 30497
49.9%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
U 2
 
< 0.1%
p 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30497
49.9%
o 30497
49.9%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
U 2
 
< 0.1%
p 2
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:17.618035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.186160802
Min length2

Characters and Unicode

Total characters66726
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSteady
4th rowSteady
5th rowNo
ValueCountFrequency (%)
no 28990
95.0%
steady 1391
 
4.6%
up 82
 
0.3%
down 59
 
0.2%
2025-04-29T05:13:17.963764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 29049
43.5%
N 28990
43.4%
S 1391
 
2.1%
t 1391
 
2.1%
e 1391
 
2.1%
a 1391
 
2.1%
d 1391
 
2.1%
y 1391
 
2.1%
U 82
 
0.1%
p 82
 
0.1%
Other values (3) 177
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 29049
43.5%
N 28990
43.4%
S 1391
 
2.1%
t 1391
 
2.1%
e 1391
 
2.1%
a 1391
 
2.1%
d 1391
 
2.1%
y 1391
 
2.1%
U 82
 
0.1%
p 82
 
0.1%
Other values (3) 177
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 29049
43.5%
N 28990
43.4%
S 1391
 
2.1%
t 1391
 
2.1%
e 1391
 
2.1%
a 1391
 
2.1%
d 1391
 
2.1%
y 1391
 
2.1%
U 82
 
0.1%
p 82
 
0.1%
Other values (3) 177
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 29049
43.5%
N 28990
43.4%
S 1391
 
2.1%
t 1391
 
2.1%
e 1391
 
2.1%
a 1391
 
2.1%
d 1391
 
2.1%
y 1391
 
2.1%
U 82
 
0.1%
p 82
 
0.1%
Other values (3) 177
 
0.3%

acetohexamide
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:18.059372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters61044
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30522
100.0%
2025-04-29T05:13:18.368410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:18.510098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.463468973
Min length2

Characters and Unicode

Total characters75190
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 26665
87.4%
steady 3450
 
11.3%
up 234
 
0.8%
down 173
 
0.6%
2025-04-29T05:13:18.928348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 26838
35.7%
N 26665
35.5%
S 3450
 
4.6%
t 3450
 
4.6%
e 3450
 
4.6%
a 3450
 
4.6%
d 3450
 
4.6%
y 3450
 
4.6%
U 234
 
0.3%
p 234
 
0.3%
Other values (3) 519
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 26838
35.7%
N 26665
35.5%
S 3450
 
4.6%
t 3450
 
4.6%
e 3450
 
4.6%
a 3450
 
4.6%
d 3450
 
4.6%
y 3450
 
4.6%
U 234
 
0.3%
p 234
 
0.3%
Other values (3) 519
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 26838
35.7%
N 26665
35.5%
S 3450
 
4.6%
t 3450
 
4.6%
e 3450
 
4.6%
a 3450
 
4.6%
d 3450
 
4.6%
y 3450
 
4.6%
U 234
 
0.3%
p 234
 
0.3%
Other values (3) 519
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 26838
35.7%
N 26665
35.5%
S 3450
 
4.6%
t 3450
 
4.6%
e 3450
 
4.6%
a 3450
 
4.6%
d 3450
 
4.6%
y 3450
 
4.6%
U 234
 
0.3%
p 234
 
0.3%
Other values (3) 519
 
0.7%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:19.060293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.373828714
Min length2

Characters and Unicode

Total characters72454
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSteady
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 27322
89.5%
steady 2771
 
9.1%
up 266
 
0.9%
down 163
 
0.5%
2025-04-29T05:13:19.518167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 27485
37.9%
N 27322
37.7%
S 2771
 
3.8%
t 2771
 
3.8%
e 2771
 
3.8%
a 2771
 
3.8%
d 2771
 
3.8%
y 2771
 
3.8%
U 266
 
0.4%
p 266
 
0.4%
Other values (3) 489
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 27485
37.9%
N 27322
37.7%
S 2771
 
3.8%
t 2771
 
3.8%
e 2771
 
3.8%
a 2771
 
3.8%
d 2771
 
3.8%
y 2771
 
3.8%
U 266
 
0.4%
p 266
 
0.4%
Other values (3) 489
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 27485
37.9%
N 27322
37.7%
S 2771
 
3.8%
t 2771
 
3.8%
e 2771
 
3.8%
a 2771
 
3.8%
d 2771
 
3.8%
y 2771
 
3.8%
U 266
 
0.4%
p 266
 
0.4%
Other values (3) 489
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 27485
37.9%
N 27322
37.7%
S 2771
 
3.8%
t 2771
 
3.8%
e 2771
 
3.8%
a 2771
 
3.8%
d 2771
 
3.8%
y 2771
 
3.8%
U 266
 
0.4%
p 266
 
0.4%
Other values (3) 489
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:19.648469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000917371
Min length2

Characters and Unicode

Total characters61072
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30515
> 99.9%
steady 7
 
< 0.1%
2025-04-29T05:13:20.065610image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30515
50.0%
o 30515
50.0%
S 7
 
< 0.1%
t 7
 
< 0.1%
e 7
 
< 0.1%
a 7
 
< 0.1%
d 7
 
< 0.1%
y 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30515
50.0%
o 30515
50.0%
S 7
 
< 0.1%
t 7
 
< 0.1%
e 7
 
< 0.1%
a 7
 
< 0.1%
d 7
 
< 0.1%
y 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30515
50.0%
o 30515
50.0%
S 7
 
< 0.1%
t 7
 
< 0.1%
e 7
 
< 0.1%
a 7
 
< 0.1%
d 7
 
< 0.1%
y 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30515
50.0%
o 30515
50.0%
S 7
 
< 0.1%
t 7
 
< 0.1%
e 7
 
< 0.1%
a 7
 
< 0.1%
d 7
 
< 0.1%
y 7
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:20.252967image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.276063168
Min length2

Characters and Unicode

Total characters69470
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 28332
92.8%
steady 2087
 
6.8%
up 64
 
0.2%
down 39
 
0.1%
2025-04-29T05:13:20.687751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 28371
40.8%
N 28332
40.8%
S 2087
 
3.0%
t 2087
 
3.0%
e 2087
 
3.0%
a 2087
 
3.0%
d 2087
 
3.0%
y 2087
 
3.0%
U 64
 
0.1%
p 64
 
0.1%
Other values (3) 117
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 28371
40.8%
N 28332
40.8%
S 2087
 
3.0%
t 2087
 
3.0%
e 2087
 
3.0%
a 2087
 
3.0%
d 2087
 
3.0%
y 2087
 
3.0%
U 64
 
0.1%
p 64
 
0.1%
Other values (3) 117
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 28371
40.8%
N 28332
40.8%
S 2087
 
3.0%
t 2087
 
3.0%
e 2087
 
3.0%
a 2087
 
3.0%
d 2087
 
3.0%
y 2087
 
3.0%
U 64
 
0.1%
p 64
 
0.1%
Other values (3) 117
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 28371
40.8%
N 28332
40.8%
S 2087
 
3.0%
t 2087
 
3.0%
e 2087
 
3.0%
a 2087
 
3.0%
d 2087
 
3.0%
y 2087
 
3.0%
U 64
 
0.1%
p 64
 
0.1%
Other values (3) 117
 
0.2%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:20.848884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.235043575
Min length2

Characters and Unicode

Total characters68218
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 28665
93.9%
steady 1782
 
5.8%
up 52
 
0.2%
down 23
 
0.1%
2025-04-29T05:13:21.416006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 28688
42.1%
N 28665
42.0%
S 1782
 
2.6%
t 1782
 
2.6%
e 1782
 
2.6%
a 1782
 
2.6%
d 1782
 
2.6%
y 1782
 
2.6%
U 52
 
0.1%
p 52
 
0.1%
Other values (3) 69
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 28688
42.1%
N 28665
42.0%
S 1782
 
2.6%
t 1782
 
2.6%
e 1782
 
2.6%
a 1782
 
2.6%
d 1782
 
2.6%
y 1782
 
2.6%
U 52
 
0.1%
p 52
 
0.1%
Other values (3) 69
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 28688
42.1%
N 28665
42.0%
S 1782
 
2.6%
t 1782
 
2.6%
e 1782
 
2.6%
a 1782
 
2.6%
d 1782
 
2.6%
y 1782
 
2.6%
U 52
 
0.1%
p 52
 
0.1%
Other values (3) 69
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 28688
42.1%
N 28665
42.0%
S 1782
 
2.6%
t 1782
 
2.6%
e 1782
 
2.6%
a 1782
 
2.6%
d 1782
 
2.6%
y 1782
 
2.6%
U 52
 
0.1%
p 52
 
0.1%
Other values (3) 69
 
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:21.560513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.010025555
Min length2

Characters and Unicode

Total characters61350
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowSteady
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30441
99.7%
steady 76
 
0.2%
up 4
 
< 0.1%
down 1
 
< 0.1%
2025-04-29T05:13:22.027886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 30442
49.6%
N 30441
49.6%
S 76
 
0.1%
t 76
 
0.1%
e 76
 
0.1%
a 76
 
0.1%
d 76
 
0.1%
y 76
 
0.1%
U 4
 
< 0.1%
p 4
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 30442
49.6%
N 30441
49.6%
S 76
 
0.1%
t 76
 
0.1%
e 76
 
0.1%
a 76
 
0.1%
d 76
 
0.1%
y 76
 
0.1%
U 4
 
< 0.1%
p 4
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 30442
49.6%
N 30441
49.6%
S 76
 
0.1%
t 76
 
0.1%
e 76
 
0.1%
a 76
 
0.1%
d 76
 
0.1%
y 76
 
0.1%
U 4
 
< 0.1%
p 4
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 30442
49.6%
N 30441
49.6%
S 76
 
0.1%
t 76
 
0.1%
e 76
 
0.1%
a 76
 
0.1%
d 76
 
0.1%
y 76
 
0.1%
U 4
 
< 0.1%
p 4
 
< 0.1%
Other values (3) 3
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:22.197074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000917371
Min length2

Characters and Unicode

Total characters61072
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30514
> 99.9%
steady 6
 
< 0.1%
down 2
 
< 0.1%
2025-04-29T05:13:22.678970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 30516
50.0%
N 30514
50.0%
S 6
 
< 0.1%
t 6
 
< 0.1%
e 6
 
< 0.1%
a 6
 
< 0.1%
d 6
 
< 0.1%
y 6
 
< 0.1%
D 2
 
< 0.1%
w 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 30516
50.0%
N 30514
50.0%
S 6
 
< 0.1%
t 6
 
< 0.1%
e 6
 
< 0.1%
a 6
 
< 0.1%
d 6
 
< 0.1%
y 6
 
< 0.1%
D 2
 
< 0.1%
w 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 30516
50.0%
N 30514
50.0%
S 6
 
< 0.1%
t 6
 
< 0.1%
e 6
 
< 0.1%
a 6
 
< 0.1%
d 6
 
< 0.1%
y 6
 
< 0.1%
D 2
 
< 0.1%
w 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 30516
50.0%
N 30514
50.0%
S 6
 
< 0.1%
t 6
 
< 0.1%
e 6
 
< 0.1%
a 6
 
< 0.1%
d 6
 
< 0.1%
y 6
 
< 0.1%
D 2
 
< 0.1%
w 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:22.802193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000262106
Min length2

Characters and Unicode

Total characters61052
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30520
> 99.9%
steady 2
 
< 0.1%
2025-04-29T05:13:23.194711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30520
50.0%
o 30520
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30520
50.0%
o 30520
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30520
50.0%
o 30520
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30520
50.0%
o 30520
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:23.367904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.001441583
Min length2

Characters and Unicode

Total characters61088
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30511
> 99.9%
steady 11
 
< 0.1%
2025-04-29T05:13:23.763793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30511
49.9%
o 30511
49.9%
S 11
 
< 0.1%
t 11
 
< 0.1%
e 11
 
< 0.1%
a 11
 
< 0.1%
d 11
 
< 0.1%
y 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30511
49.9%
o 30511
49.9%
S 11
 
< 0.1%
t 11
 
< 0.1%
e 11
 
< 0.1%
a 11
 
< 0.1%
d 11
 
< 0.1%
y 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30511
49.9%
o 30511
49.9%
S 11
 
< 0.1%
t 11
 
< 0.1%
e 11
 
< 0.1%
a 11
 
< 0.1%
d 11
 
< 0.1%
y 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30511
49.9%
o 30511
49.9%
S 11
 
< 0.1%
t 11
 
< 0.1%
e 11
 
< 0.1%
a 11
 
< 0.1%
d 11
 
< 0.1%
y 11
 
< 0.1%

examide
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:23.870115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters61044
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30522
100.0%
2025-04-29T05:13:24.229922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

citoglipton
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:24.342853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters61044
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30522
100.0%
2025-04-29T05:13:24.753253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:24.947960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length3.46071686
Min length2

Characters and Unicode

Total characters105628
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowSteady
5th rowNo
ValueCountFrequency (%)
no 14170
46.4%
steady 9296
30.5%
down 3700
 
12.1%
up 3356
 
11.0%
2025-04-29T05:13:25.452494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 17870
16.9%
N 14170
13.4%
S 9296
8.8%
t 9296
8.8%
e 9296
8.8%
a 9296
8.8%
d 9296
8.8%
y 9296
8.8%
D 3700
 
3.5%
w 3700
 
3.5%
Other values (3) 10412
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 17870
16.9%
N 14170
13.4%
S 9296
8.8%
t 9296
8.8%
e 9296
8.8%
a 9296
8.8%
d 9296
8.8%
y 9296
8.8%
D 3700
 
3.5%
w 3700
 
3.5%
Other values (3) 10412
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 17870
16.9%
N 14170
13.4%
S 9296
8.8%
t 9296
8.8%
e 9296
8.8%
a 9296
8.8%
d 9296
8.8%
y 9296
8.8%
D 3700
 
3.5%
w 3700
 
3.5%
Other values (3) 10412
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 17870
16.9%
N 14170
13.4%
S 9296
8.8%
t 9296
8.8%
e 9296
8.8%
a 9296
8.8%
d 9296
8.8%
y 9296
8.8%
D 3700
 
3.5%
w 3700
 
3.5%
Other values (3) 10412
9.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:25.605212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.027586659
Min length2

Characters and Unicode

Total characters61886
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30307
99.3%
steady 209
 
0.7%
up 3
 
< 0.1%
down 3
 
< 0.1%
2025-04-29T05:13:26.041749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 30310
49.0%
N 30307
49.0%
S 209
 
0.3%
t 209
 
0.3%
e 209
 
0.3%
a 209
 
0.3%
d 209
 
0.3%
y 209
 
0.3%
U 3
 
< 0.1%
p 3
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 30310
49.0%
N 30307
49.0%
S 209
 
0.3%
t 209
 
0.3%
e 209
 
0.3%
a 209
 
0.3%
d 209
 
0.3%
y 209
 
0.3%
U 3
 
< 0.1%
p 3
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 30310
49.0%
N 30307
49.0%
S 209
 
0.3%
t 209
 
0.3%
e 209
 
0.3%
a 209
 
0.3%
d 209
 
0.3%
y 209
 
0.3%
U 3
 
< 0.1%
p 3
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 30310
49.0%
N 30307
49.0%
S 209
 
0.3%
t 209
 
0.3%
e 209
 
0.3%
a 209
 
0.3%
d 209
 
0.3%
y 209
 
0.3%
U 3
 
< 0.1%
p 3
 
< 0.1%
Other values (3) 9
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:26.174745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000262106
Min length2

Characters and Unicode

Total characters61052
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30520
> 99.9%
steady 2
 
< 0.1%
2025-04-29T05:13:26.688309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30520
50.0%
o 30520
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30520
50.0%
o 30520
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30520
50.0%
o 30520
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30520
50.0%
o 30520
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:26.841954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000131053
Min length2

Characters and Unicode

Total characters61048
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30521
> 99.9%
steady 1
 
< 0.1%
2025-04-29T05:13:27.338669image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30521
50.0%
o 30521
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30521
50.0%
o 30521
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30521
50.0%
o 30521
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30521
50.0%
o 30521
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:27.443009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters61044
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30522
100.0%
2025-04-29T05:13:27.724596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30522
50.0%
o 30522
50.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:27.843501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000131053
Min length2

Characters and Unicode

Total characters61048
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 30521
> 99.9%
steady 1
 
< 0.1%
2025-04-29T05:13:28.237990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30521
50.0%
o 30521
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30521
50.0%
o 30521
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30521
50.0%
o 30521
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30521
50.0%
o 30521
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

change
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:28.390759image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters61044
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowCh
4th rowCh
5th rowNo
ValueCountFrequency (%)
no 16402
53.7%
ch 14120
46.3%
2025-04-29T05:13:28.727211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 16402
26.9%
o 16402
26.9%
C 14120
23.1%
h 14120
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 16402
26.9%
o 16402
26.9%
C 14120
23.1%
h 14120
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 16402
26.9%
o 16402
26.9%
C 14120
23.1%
h 14120
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61044
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 16402
26.9%
o 16402
26.9%
C 14120
23.1%
h 14120
23.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:28.848157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.771443549
Min length2

Characters and Unicode

Total characters84590
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes
ValueCountFrequency (%)
yes 23546
77.1%
no 6976
 
22.9%
2025-04-29T05:13:29.244621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 23546
27.8%
e 23546
27.8%
s 23546
27.8%
N 6976
 
8.2%
o 6976
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 23546
27.8%
e 23546
27.8%
s 23546
27.8%
N 6976
 
8.2%
o 6976
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 23546
27.8%
e 23546
27.8%
s 23546
27.8%
N 6976
 
8.2%
o 6976
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 23546
27.8%
e 23546
27.8%
s 23546
27.8%
N 6976
 
8.2%
o 6976
 
8.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:29.380293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.461339362
Min length2

Characters and Unicode

Total characters75125
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd rowNO
3rd row>30
4th row>30
5th rowNO
ValueCountFrequency (%)
no 16441
53.9%
30 14081
46.1%
2025-04-29T05:13:29.708916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 16441
21.9%
O 16441
21.9%
3 14081
18.7%
0 14081
18.7%
> 10652
14.2%
< 3429
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 16441
21.9%
O 16441
21.9%
3 14081
18.7%
0 14081
18.7%
> 10652
14.2%
< 3429
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 16441
21.9%
O 16441
21.9%
3 14081
18.7%
0 14081
18.7%
> 10652
14.2%
< 3429
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 16441
21.9%
O 16441
21.9%
3 14081
18.7%
0 14081
18.7%
> 10652
14.2%
< 3429
 
4.6%

split
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-29T05:13:29.853829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters152610
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrain
2nd rowtrain
3rd rowtrain
4th rowtrain
5th rowtrain
ValueCountFrequency (%)
train 30522
100.0%
2025-04-29T05:13:30.173847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 30522
20.0%
r 30522
20.0%
a 30522
20.0%
i 30522
20.0%
n 30522
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 30522
20.0%
r 30522
20.0%
a 30522
20.0%
i 30522
20.0%
n 30522
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 30522
20.0%
r 30522
20.0%
a 30522
20.0%
i 30522
20.0%
n 30522
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 30522
20.0%
r 30522
20.0%
a 30522
20.0%
i 30522
20.0%
n 30522
20.0%

load_date
Date

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2025-04-29 00:00:00
Maximum2025-04-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-29T05:13:30.431562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T05:13:30.606435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)